What do we want from machine intelligence? We envision machines that are not just tools
for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and …

Automatic differentiation (AD), a technique for constructing new programs which compute
the derivative of an original program, has become ubiquitous throughout scientific …

Automatic differentiation is a popular technique for computing derivatives of computer
programs. While automatic differentiation has been successfully used in countless …

Among the well-known methods to approximate derivatives of expectancies computed by
Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to …

This paper introduces SMCP3, a method for automatically implementing custom sequential
Monte Carlo samplers for inference in probabilistic programs. Unlike particle filters and …

We show that a maximum likelihood approach for parameter estimation in agent-based
models (ABMs) of opinion dynamics outperforms the typical simulation-based approach …

Programs involving discontinuities introduced by control flow constructs such as conditional
branches pose challenges to mathematical optimization methods that assume a degree of …

We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers,
allowing us to differentiate through probabilistic inference, even if the model has discrete …

We introduce a new setting, the category of ωPAP spaces, for reasoning denotationally
about expressive differentiable and probabilistic programming languages. Our semantics is …

We present a new approach to the design and implementation of probabilistic programming
languages (PPLs), based on the idea of stochastically estimating the probability density …